The present invention relates to an adjustment rule generating method and apparatus for generating an adjustment rule for appropriately and easily adjusting an input to a multiple-input/output system having nonlinear characteristics to obtain a desired output from the system, and an adjustment control method and apparatus for adjusting the input to the system using the generated adjustment rule.
In an adjustment operation at a plant, a device production line, or a maintenance operation, when a certain element in the system is adjusted, a plurality of other elements vary upon adjustment, so it is often difficult to properly adjust all elements.
How to adjust an input to obtain a desired output is a general problem. To solve this problem, various means have been implemented conventionally.
In fact, the problem of an adjustment parameter (to be simply referred to as a parameter hereinafter) and the output is often concomitant with the original input/output relationship. For this reason, an effective result can hardly be obtained.
As reasons for this, the following three main factors are considered.
1. Complex correlation between the parameter and the output
2. Nonlinearity of the parameter and output
3. Maldistribution of data
Both the parameter and the output are generally multidimensional rather than one-dimensional (variable) and have complex causality. The relationship between the parameter and the output is not linear. Resultant data is small in quantity or maldistributed, so the characteristics between the parameter and the output cannot be sufficiently described using such data. It can be supposed that these factors make the problem difficult to solve.
To solve this problem, not only means based on the theory of linear mathematics but also means reflecting the farsighted knowledge or intuition of persons who have been concerned in actual adjustment have been used. For example, a method using fuzzy inference or qualitative causality reasoning is used.
The fuzzy inference can be effective for a system having nonlinear characteristics. However, the fuzzy inference is regarded eventually xe2x80x9csuccessfulxe2x80x9d only when the membership function or adjustment rule can be appropriately defined.
Generally, the fuzzy theory is applied to a nonlinear system. However, analogical reasoning can hardly be made because the response from an object is not linear. In addition, trial and error in system identification also tends to be cumbersome. Even when the system can be identified using a nonlinear model, the input amount for adjustment (manipulated variable for control) is hard to calculate because of the nonlinear model. This results in a difficulty in setting the membership function or adjustment rule. Furthermore, it cannot be guaranteed that the initial rule is still effective for a variation in system characteristics.
Essentially, this also applies to qualitative causality reasoning. Once the causality is clarified, analysis is automatically performed by a computer. However, data in checking the causality depends on human determination, like the fuzzy inference. More specifically, even when data is to be semi-automatically processed and modeled, the human data determination reference must be defined in advance. In this respect, the qualitative causality reasoning is essentially identical to the fuzzy inference (e.g., Jpn. Pat. Appln. KOKAI Publication No. 7-191706).
In reasoning based on causality, normally, the current state is analyzed on the basis of past data (past events). This processing requires a large quantity of past data. This method is convenient when a relatively large plant (system) is operated for a long time. However, when an individual difference between objects is assumed as in adjusting parameters of individual products on a production line, or when adjustment is to be made in response to an environmental change, the number of data is limited because adjustment cannot always depend on other individual data. Therefore, adjustment can hardly be performed using the method based on the conventional event data.
Reasoning does not suffice for adjustment. Unlike system observation based on two references, e.g., faulty diagnosis for checking whether the interior of a system is faulty or not (subsequent processing is left to human operations), some action must be taken for the system after situation determination in the control system.
The present invention has been made in consideration of the above situation, and has as its object to provide a system having the following characteristic features.
1. Adjustment is performed while sampling data
2. A large quantity of data is not required in advance
3. Nonlinear characteristics can be coped with.
More specifically, the present invention has as its object to provide an adjustment rule generating method and apparatus for generating an adjustment rule to adjust an object having multiple variables (multiple-input/output system) whose correlation has complex nonlinear characteristics.
It is another object of the present invention to provide an adjustment control method and apparatus for adjusting an object in accordance with a generated adjustment rule.
According to the present invention, the adjustment operation can be appropriately standardized and automated.
The adjustment rule generating method and apparatus of the present invention are characterized in that a table (dependency relationship table) representing qualitative characteristics is assumed in which inputs (to be referred to as manipulated variables hereinafter) are classified in units of change patterns of outputs (to be referred to as controlled variables hereinafter) having influence, and
an operation procedure (to be referred to as an adjustment rule hereinafter) for adjustment is generated.
The adjustment control method and apparatus according to the present invention are characterized in that it is determined whether the current object situation exhibits an exceptional behavior (vibration/saturation), on the basis of an instruction (selection of an adjusted controlled variable and a manipulated variable) obtained from an automatically generated adjustment rule and a past operation in response to an occasionally output deviation. If it is determined that no exceptional behavior is observed, the instruction of the generated adjustment rule is executed; otherwise, the correction amount of the manipulated variable which is input to the object to be adjusted is given assuming that a predetermined input operation is performed.
(1) An adjustment rule generating apparatus which determines the manipulated variable of the adjustment object or sets the value of a variable parameter (the variable parameter will not particularly be discriminated from the manipulated variable hereinafter) of an adjustment object such that a controlled variable within an allowable range can be obtained, is characterized by comprising
adjustable controlled variable selection means for receiving a change in controlled variable corresponding to each manipulated variable of the adjustment object and qualitative feature data of a change difference between controlled variables and defining some manipulated variables which can be independently adjusted from the feature data in units of controlled variables, and adjustment rule format generating means for converting adjustable controlled variable data output from the adjustable controlled variable selection means in units of manipulated variables into a predetermined format and outputting the format as an adjustment procedure.
(2) The adjustment rule generating apparatus of arrangement (1) is characterized in that the change in controlled variable corresponding to each manipulated variable of the adjustment object is defined by input data (manipulated variable characteristics and input/output dependency relationship table; to be referred to as a dependency table hereinafter) as binary data which describes whether each manipulated variable affects the controlled variable and binary data of a change pattern given by the manipulated variable to the controlled variable and expressing the qualitative feature data of the change difference between controlled variables.
(3) An adjustment control apparatus for performing a proportional operation is characterized by comprising
deviation data generating means for calculating a deviation of a controlled variable of an adjustment object and outputting the deviation, adjustment rule storage means for receiving the controlled variable deviation obtained from the deviation data generating means and storing an adjustment rule obtained by the apparatus of arrangement (1) or (2), application rule selection means for receiving the controlled variable deviation calculated by the deviation data generating means and the adjustment rule stored in the adjustment rule storage means, selecting a manipulated variable to be adjusted, and defining the selected manipulated variable as an application rule, and manipulated variable determination means for determining a correction amount of the manipulated variable selected by the application rule selection means with reference to the controlled variable deviation as a predetermined proportional amount of the deviation of the controlled variable corresponding to the manipulated variable defined by the application rule.
(4) The adjustment control apparatus of arrangement (3) which performs a proportional operation and nonlinear avoidance is characterized by further comprising adjustment history data storage means for recording/updating adjustment history data (adjustment count, manipulated variable, controlled variable deviation, and the like), and in that
the manipulated variable determination means refers to the controlled variable deviation and the adjustment history data stored in the adjustment history data storage means in accordance with the application rule selected by the application rule selection means to determine the correction amount of the manipulated variable of the application rule or a manipulated variable other than the manipulated variable as a proportional amount of the controlled variable deviation or a relative difference from another controlled variable deviation, or independently of the proportional amount, newly stores the determined manipulated variable or controlled variable deviation data referred to in determining the manipulated variable in the adjustment history data storage means, and updates the adjustment history data.
(5) The adjustment control apparatus of arrangement (4) which performs a test operation and nonlinear avoidance is characterized in that
the application rule determination means also discriminates between test adjustment and actual adjustment for identifying characteristics of the object with reference to the adjustment history data stored in the adjustment history data storage means, and
the manipulated variable determination means refers to data obtained from the adjustment history data storage means and the current controlled variable deviation of the adjustment object to determine a manipulated variable for test adjustment or actual adjustment, newly stores the determined manipulated variable or controlled variable deviation data referred to in determining the manipulated variable in the adjustment history data storage means, and updates the adjustment history data.
(6) An adjustment possibility evaluation apparatus is characterized by comprising
an input unit for inputting the adjustment rule obtained from the adjustment rule generating apparatus of arrangement (1) or (2),
rule candidate initial setting means for generating some adjustment rules in which manipulated variables and controlled variables are in one-to-one correspondence,
controlled variable selection means for selecting a controlled variable to check whether adjustment is enabled for each candidate rule set by the rule candidate initial setting means,
corresponding manipulated variable search means for searching for a manipulated variable which corresponds to the controlled variable selected by the controlled variable selection means and can adjust the controlled variable,
rule candidate generating means for storing the candidate rule as a rule candidate when all the controlled variables can be adjusted on the basis of the candidate rule set by the rule candidate initial setting means, and
rule group generating means for outputting a rule group while omitting the same rule candidate stored in the rule candidate generating means.
(7) An adjustment rule candidate generating apparatus for preparing a dependency table and an adjustment rule is characterized by comprising
dependency table candidate generating means for generating some dependency table candidates defined in arrangement (3) from actual input/output data of the adjustment object, adjustment rule generating means of arrangement (3), which receives each dependency table candidate to acquire an adjustment rule corresponding to the dependency table candidate, and dependency table/rule candidate storage means for storing the adjustment rule obtained from the adjustment rule generating apparatus in correspondence with the dependency table candidate.
In the adjustment rule generating apparatus of arrangement (1),
the change in controlled variable corresponding to each manipulated variable of the adjustment object and qualitative feature data of a change difference between controlled variables are input to the adjustable controlled variable selection means,
the adjustable controlled variable selection means defines some manipulated variables which can be independently adjusted in units of controlled variables from the received feature data and outputs adjustable controlled variable data representing the relationship between the manipulated variable and the controlled variable, and
the adjustment rule format generating means converts the adjustable controlled variable data output from the adjustable controlled variable selection means in units of manipulated variables into a predetermined format and outputs adjustment procedure data (adjustment rule).
In the arrangement (2), the change in controlled variable corresponding to each manipulated variable of the adjustment object is defined by input data (manipulated variable characteristics and input/output dependency relationship table; to be referred to as a dependency table hereinafter) as binary data which describes whether each manipulated variable affects the controlled variable and binary data of a change pattern given by the manipulated variable to the controlled variable and expressing the qualitative feature data of the change difference between controlled variables,
the adjustable controlled variable selection means defines some manipulated variables which can be independently adjusted in units of controlled variables from the received feature data and outputs adjustable controlled variable data representing the relationship between the manipulated variable and the controlled variable, and
the adjustment rule format generating means converts the adjustable controlled variable data output from the adjustable controlled variable selection means in units of manipulated variables into a predetermined format and outputs adjustment procedure data (adjustment rule).
In the adjustment control apparatus of arrangement (3) which performs a proportional operation,
the deviation data generating means calculates a deviation of a controlled variable of an adjustment object,
the adjustment rule storage means stores an adjustment rule obtained by the adjustment rule generating apparatus of arrangement (1) or (2),
the application rule selection means receives the controlled variable deviation and the adjustment rule stored in the adjustment rule storage means, selects a manipulated variable to be adjusted, and outputs it as an application rule, and
the manipulated variable determination means determines a correction amount of the manipulated variable selected by the application rule selection means with reference to the controlled variable deviation as a predetermined proportional amount of the deviation of the controlled variable corresponding to the manipulated variable defined by the application rule.
In the adjustment control apparatus of arrangement (4) which performs a proportional operation and nonlinear avoidance,
the deviation data generating means calculates a deviation of a controlled variable of the adjustment object,
the adjustment history data storage means records/updates adjustment history data (adjustment count, manipulated variable, controlled variable deviation, and the like),
the application rule selection means determines an application rule from the adjustment rules for adjustment, and
the manipulated variable determination means refers to the controlled variable deviation and the adjustment history data stored in the adjustment history data storage means to determine the correction amount of the manipulated variable according to the application rule or a manipulated variable other than the manipulated variable as a proportional amount of the controlled variable deviation or a relative difference from another controlled variable deviation, or independently of the proportional amount, newly stores the determined manipulated variable or controlled variable deviation data referred to in determining the manipulated variable in the adjustment history data storage means, and updates the adjustment history data.
In the adjustment control apparatus of arrangement (5) which performs a test operation and nonlinear avoidance,
the deviation data generating means calculates a deviation of a controlled variable of the adjustment object,
the application rule determination means discriminates between test adjustment and actual adjustment for identifying characteristics of the object with reference to the adjustment history data stored in the adjustment history data storage means, and
the manipulated variable determination means refers to data obtained from the adjustment history data storage means and the current controlled variable deviation of the adjustment object to determine a manipulated variable for test adjustment or actual adjustment, newly stores the determined manipulated variable or controlled variable deviation data referred to in determining the manipulated variable in the adjustment history data storage means, and updates the adjustment history data.
In the adjustment possibility evaluation apparatus of arrangement (6),
the adjustment rule obtained from the adjustment rule generating apparatus of arrangement (1) or (2) is obtained as an input,
the rule candidate initial setting means generates some adjustment rules in which manipulated variables and controlled variables are in one-to-one correspondence,
the controlled variable selection means selects a controlled variable to check whether adjustment is enabled for each candidate rule set by the rule candidate initial setting means,
the corresponding manipulated variable search means searches for a manipulated variable which corresponds to the controlled variable selected by the controlled variable selection means and can adjust the controlled variable,
the rule candidate generating means stores the candidate rule as a rule candidate when all the controlled variables can be adjusted on the basis of the candidate rule set by the rule candidate initial setting means, and
the rule group generating means outputs a rule group while omitting the same rule candidate stored in the rule candidate generating means.
In the adjustment rule candidate generating apparatus of arrangement (7) which prepares a dependency table and an adjustment rule,
the dependency table candidate generating means generates some dependency table candidates defined in arrangement (3) from actual input/output data of the adjustment object, the adjustment rule generating apparatus of arrangement (3) receives each dependency table candidate and generates an adjustment rule corresponding to the dependency table candidate, and the dependency table/rule candidate storage means stores the adjustment rule obtained from the adjustment rule generating apparatus in correspondence with the dependency table candidate.
According to the present invention, there is also provided an adjustment rule generating apparatus which determines a second data group such that a first data group corresponding to a predetermined object has a desired value, characterized by comprising
adjustable controlled variable selection means for obtaining a change pattern in units of outputs of controlled variables affected by the manipulated variable using actual data of the object, receiving, as an input, feature data representing qualitative characteristics classified in accordance with the change pattern, and defining some manipulated variables which can adjust one or more controlled variables including the controlled variable, in units of controlled variables, on the basis of the feature data and the influence of the manipulated variable and the controlled variable, and
adjustment rule format generating means for converting the adjustable controlled variable data output from the adjustable controlled variable selection means into a predetermined format in units of manipulated variables on the basis of the feature data and the influence of the manipulated variable and the controlled variable and outputting the format as an adjustment procedure.
There is also provided an adjustment rule generating apparatus which determines a second data group such that a first data group corresponding to a predetermined object has a desired value, characterized by comprising
adjustable controlled variable selection means for receiving a change in controlled variable corresponding to each manipulated variable of the object and qualitative feature data of a change difference between controlled variables and defining one or more manipulated variables which can adjust one or more controlled variables including the controlled variable, in units of controlled variables, on the basis of the feature data and the influence of the manipulated variable and the controlled variable, and
adjustment rule format generating means for converting the adjustable controlled variable data output from the adjustable controlled variable selection means into a predetermined format in units of manipulated variables on the basis of the feature data and the influence of the manipulated variable and the controlled variable and outputting the format as an adjustment procedure.
The change in controlled variable corresponding to each manipulated variable of the adjustment. object is defined by input data as binary data which describes whether each manipulated variable affects the controlled variable and binary data of a change pattern given by the manipulated variable to the controlled variable and expressing the qualitative feature data of the change difference between controlled variables.
An adjustment possibility evaluation apparatus comprises
rule candidate initial setting means for receiving an adjustment rule obtained from one of the adjustment rule generating apparatuses and generating some adjustment rules in which manipulated variables and controlled variables are in one-to-one correspondence,
controlled variable selection means for selecting a controlled variable to check whether adjustment is enabled for each candidate rule set by the rule candidate initial setting means,
corresponding manipulated variable search means for searching for a manipulated variable which corresponds to the controlled variable selected by the controlled variable selection means and capable of adjusting the controlled variable,
rule candidate generating means for storing the candidate rule as a rule candidate when all the controlled variables can be adjusted on the basis of the candidate rule set by the rule candidate initial setting means, and
rule group generating means for outputting a rule group while omitting the same rule candidate stored in the rule candidate generating means.
There is also provided an adjustment rule generating method of determining a second data group such that a first data group corresponding to a predetermined object has a desired value, characterized by comprising
on the basis of data obtained on the basis of the object, obtaining predetermined first data affected by predetermined second data and change characteristics between the predetermined first data,
selecting specific one of the first data, which has change characteristics between outputs capable of correcting the change characteristics between first data, and
determining specific second data which can correspond to the selected specific first data from the second data group.
According to the present invention, there is also provided an adjustment rule generating method of determining a second data group such that a first data group corresponding to a predetermined object has a desired value, characterized by comprising
receiving a change in controlled variable corresponding to each manipulated variable of the object and qualitative feature data of a change difference between controlled variables and defining one or more manipulated variables which can adjust one or more controlled variables including the controlled variable, in units of controlled variables, on the basis of the feature data and the influence of the manipulated variable and the controlled variable,
converting the obtained adjustable controlled variable data into a predetermined format in units of manipulated variables on the basis of the feature data and the influence of the manipulated variable and the controlled variable and outputting the format as an adjustment procedure,
calculating a deviation of the controlled variable of the object and outputting the deviation,
receiving a controlled variable deviation obtained from the output and storing an obtained adjustment rule,
receiving the calculated controlled variable deviation and the stored adjustment rule, selecting a manipulated variable to be adjusted, and defining the manipulated variable as an application rule, and
determining a correction amount of the manipulated variable selected by the application rule selection unit with reference to the controlled variable deviation as a predetermined proportional amount of the deviation of the controlled variable corresponding to the manipulated variable defined by the application rule.
Additional objects and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objects and advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the appended claims.